Martin Florian P, Goronflot Thomas, Moyer Jean D, Huet Olivier, Asehnoune Karim, Cinotti Raphaël, Gourraud Pierre A, Roquilly Antoine
Nantes Université, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR 1064, Center for Research in Transplantation and Translational Immunology (CR2TI), 22 Boulevard Bénoni Goullin, 44200, Nantes, France.
Department of Anesthesiology and Surgical Intensive Care Unit, Centre Hospitalier Universitaire (CHU) Nantes, Nantes, France.
Neurocrit Care. 2025 Apr;42(2):573-586. doi: 10.1007/s12028-024-02082-3. Epub 2024 Aug 13.
The prognostication of long-term functional outcomes remains challenging in patients with traumatic brain injury (TBI). Our aim was to demonstrate that intensive care unit (ICU) variables are not efficient to predict 6-month functional outcome in survivors with moderate to severe TBI (msTBI) but are mostly associated with mortality, which leads to a mortality bias for models predicting a composite outcome of mortality and severe disability.
We analyzed the data from the multicenter randomized controlled Continuous Hyperosmolar Therapy in Traumatic Brain-Injured Patients trial and developed predictive models using machine learning methods and baseline characteristics and predictors collected during ICU stay. We compared our models' predictions of 6-month binary Glasgow Outcome Scale extended (GOS-E) score in all patients with msTBI (unfavorable GOS-E 1-4 vs. favorable GOS-E 5-8) with mortality (GOS-E 1 vs. GOS-E 2-8) and binary functional outcome in survivors with msTBI (severe disability GOS-E 2-4 vs. moderate to no disability GOS-E 5-8). We investigated the link between ICU variables and long-term functional outcomes in survivors with msTBI using predictive modeling and factor analysis of mixed data and validated our hypotheses on the International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) model.
Based on data from 370 patients with msTBI and classically used ICU variables, the prediction of the 6-month outcome in survivors was inefficient (mean area under the receiver operating characteristic 0.52). Using factor analysis of mixed data graph, we demonstrated that high-variance ICU variables were not associated with outcome in survivors with msTBI (p = 0.15 for dimension 1, p = 0.53 for dimension 2) but mostly with mortality (p < 0.001 for dimension 1), leading to a mortality bias for models predicting a composite outcome of mortality and severe disability. We finally identified this mortality bias in the IMPACT model.
We demonstrated using machine learning-based predictive models that classically used ICU variables are strongly associated with mortality but not with 6-month outcome in survivors with msTBI, leading to a mortality bias when predicting a composite outcome of mortality and severe disability.
对于创伤性脑损伤（TBI）患者，长期功能预后的预测仍然具有挑战性。我们的目的是证明重症监护病房（ICU）变量在预测中度至重度TBI（msTBI）幸存者6个月功能预后方面效率不高，但大多与死亡率相关，这导致预测死亡率和严重残疾复合结局的模型存在死亡率偏差。
我们分析了多中心随机对照试验“创伤性脑损伤患者持续高渗疗法”的数据，并使用机器学习方法以及ICU住院期间收集的基线特征和预测指标建立了预测模型。我们将模型对所有msTBI患者6个月二元格拉斯哥预后评分扩展版（GOS-E）（不良GOS-E 1-4与良好GOS-E 5-8）的预测与死亡率（GOS-E 1与GOS-E 2-8）以及msTBI幸存者的二元功能预后（严重残疾GOS-E 2-4与中度至无残疾GOS-E 5-8）进行了比较。我们使用预测建模和混合数据的因子分析研究了ICU变量与msTBI幸存者长期功能预后之间的联系，并在创伤性脑损伤国际临床试验预后与分析任务组（IMPACT）模型上验证了我们的假设。
基于370例msTBI患者的数据和经典使用的ICU变量，对幸存者6个月结局的预测效率低下（受试者操作特征曲线下平均面积为0.52）。使用混合数据图的因子分析，我们证明高方差ICU变量与msTBI幸存者的结局无关（维度1的p = 0.15，维度2的p = 0.53），但大多与死亡率相关（维度1的p < 0.001），这导致预测死亡率和严重残疾复合结局的模型存在死亡率偏差。我们最终在IMPACT模型中发现了这种死亡率偏差。
我们使用基于机器学习的预测模型证明，经典使用的ICU变量与死亡率密切相关，但与msTBI幸存者6个月结局无关，在预测死亡率和严重残疾复合结局时会导致死亡率偏差。
